In recent years, more emphasis is placed on the performance of massive databases. It is often required not only that database systems provide high throughputs with rapid response times, but also that they are fully available 24-hours-per-day and 7-days-per-week. Requirements for throughput and response time can be satisfied by upgrading the hardware. As a result, databases in the old hardware environment have to be moved to the new one. Moving a database, however, generally requires taking the database off line for a long time, which is unacceptable for numerous applications. In this paper, a very practical and important subject is addressed: how to upgrade the hardware on line, i.e., how to move a database from an old hardware environment to a new one concurrently with users' reading and writing of the database. A technique for this purpose is proposed. We have implemented a prototype based on this technique. Our experiments with the prototype shown that compared with conventional off-line approach, the proposed technique could give a performance improvement by more than 85% in the query-bound environment and 40% in the update-bound environment.

Data mining across different companies, organizations, online shops, or the likes is necessary so as to discover valuable shared patterns, associations, trends, or dependencies in their shared data. Privacy, however, is a concern. In many situations it is required that data mining should be conducted without any privacy being violated. In response to this requirement, in this paper we propose an effective distributed privacy-preserving data mining approach called SDDM. SDDM is characterized by its ability to resist collusion. Unless the number of colluding sites in a distributed system is larger than or equal to 4, privacy cannot be violated. Results of performance study demonstrated the effectiveness of SDDM.

In order for the agent-based applications to be truly autonomous and decentralized, heterogeneous multi-agent systems themselves must communicate and interoperate with each other. To solve the problem, we take a two-step approach. First, message-level interoperability is realized by a gateway agent that interconnects heterogeneous multi-agent systems. Second, higher-level interoperation of conversations, which consist of bi-directional streams of messages, is achieved by dynamically negotiating the interaction protocols. We demonstrate the concept, technique and implementation of integrating multi-agent systems and show how the method improves the assurance of real-world applications in autonomous decentralized systems.